Perseus: A Simple High-Order Regularization Method for Variational Inequalities

Tianyi Lin, Michael. I. Jordan

Published 2022-05-06Version 1

This paper settles an open and challenging question pertaining to the design of simple high-order regularization methods for solving smooth and monotone variational inequalities (VIs). A VI involves finding $x^\star \in \mathcal{X}$ such that $\langle F(x), x - x^\star\rangle \geq 0$ for all $x \in \mathcal{X}$ and we consider the setting where $F: \mathbb{R}^d \mapsto \mathbb{R}^d$ is smooth with up to $(p-1)^{th}$-order derivatives. For the case of $p = 2$,~\citet{Nesterov-2006-Constrained} extended the cubic regularized Newton's method to VIs with a global rate of $O(\epsilon^{-1})$. \citet{Monteiro-2012-Iteration} proposed another second-order method which achieved an improved rate of $O(\epsilon^{-2/3}\log(1/\epsilon))$, but this method required a nontrivial binary search procedure as an inner loop. High-order methods based on similar binary search procedures have been further developed and shown to achieve a rate of $O(\epsilon^{-2/(p+1)}\log(1/\epsilon))$. However, such search procedure can be computationally prohibitive in practice and the problem of finding a simple high-order regularization methods remains as an open and challenging question in optimization theory. We propose a $p^{th}$-order method which does \textit{not} require any binary search scheme and is guaranteed to converge to a weak solution with a global rate of $O(\epsilon^{-2/(p+1)})$. A version with restarting attains a global linear and local superlinear convergence rate for smooth and strongly monotone VIs. Further, our method achieves a global rate of $O(\epsilon^{-2/p})$ for solving smooth and non-monotone VIs satisfying the Minty condition; moreover, the restarted version again attains a global linear and local superlinear convergence rate if the strong Minty condition holds.